Implicit scene modelling from imprecise point clouds

In applying optical methods for automated 3D indoor modelling, the 3D reconstruction of objects and surfaces is very sensitive to both lighting conditions and the observed surface properties, which ultimately compromise the utility of the acquired 3D point clouds. This paper presents a robust scene reconstruction method which is predicated upon the observation that most objects contain only a small set of primitives. The approach combines sparse approximation techniques from the compressive sensing domain with surface rendering approaches from computer graphics. The amalgamation of these techniques allows a scene to be represented by a small set of geometric primitives and to generate perceptually appealing results. The resulting scene surface models are defined as implicit functions and may be processed using conventional rendering algorithms such as marching cubes, to deliver polygonal models of arbitrary resolution. It will also be shown that 3D point clouds with outliers, strong noise and varying sampling density can be reliably processed without manual intervention

Region-of-Interest Prioritised Sampling for Constrained Autonomous Exploration Systems

Goal oriented autonomous operation of space rovers has been known to increase scientific output of a mission. In this work we present an algorithm, called the RoI Prioritised Sampling (RPS), that prioritises Region-of-Interests (RoIs) in an exploration scenario in order to utilise the limited resources of the imaging instrument on the rover effectively. This prioritisation is based on an estimator that evaluates the change in information content at consecutive spatial scales of the RoIs without calculating the finer scale reconstruction. The estimator, called the Refinement Indicator (RI), is motivated and derived. Multi-scale acquisition approaches, based on classical and multilevel compressed sensing, with respect to the single pixel camera architecture are discussed. The performance of the algorithm is verified on remote sensing images and compared with the state-of-the-art multi-resolution reconstruction algorithms. At the considered sub-sampling rates the RPS is shown to better utilise the system resources for reconstructing the RoIs

Visual recognition of bridges by using stereo cameras on trains

Recognition of either patterns or objects in mobile systems continues to be in the focus of intensive research, with many applications being enhanced by integrating environment related information. This paper presents a practical technique for detecting and recognizing bridges from a train using a stereo camera which provides depth and grayscale images. The algorithm has been applied to a train system, where object detection combined with a given map of an area is used to improve localization. The approach is based on the detection of primitive features including edges and corners in the depth image. The pairwise spatial relations between the features are then modeled by a graph, so the classification and detection can be performed by a probabilistic Markov Random Field framework. The algorithm has been tested on the real-life datasets of the Rail Collision Avoidance System (RCAS) project. The presented results prove the applicability of the framework for detection of objects by exploiting geometrical appearance constraints

Camera Condition Monitoring and Readjustmentby means of Noise and Blur

Autonomous vehicles and robots require increasingly more robustness and reliability to meet the demands of modern tasks. These requirements specially apply to cameras onboard such vehicles because they are the predominant sensors to acquire information about the environment and support actions. Cameras must maintain proper functionality and take automatic countermeasures if necessary. However, there is little work that examines the practical use of a general condition monitoring approach for cameras and designs countermeasures in the context of an envisaged high-level application. We propose a generic and interpretable self-health-maintenance framework for cameras based on data- and physically-grounded models. To this end, we determine two reliable, real-time capable estimators for typical image effects of a camera in poor condition (defocus blur, motion blur, different noise phenomena and most common combinations) by comparing traditional and retrained machine learning-based approaches in extensive experiments. Furthermore, we demonstrate how one can adjust the camera parameters to achieve optimal whole-system capability based on experimental (non-linear and non-monotonic) input-output performance curves, using object detection, motion blur and sensor noise as examples. Our framework not only provides a practical ready-to-use solution to evaluate and maintain the health of cameras, but can also serve as a basis for extensions to tackle more sophisticated problems that combine additional data sources (e.g., sensor or environment parameters) empirically in order to attain fully reliable and robust machines

The Venus Emissivity Mapper Concept

Based on experience gained from using the VIRTIS instrument on Venus Express to observe the surface of Venus and the new high temperature laboratory experiments, we have developed the multispectral Venus Emissivity Mapper (VEM) to study the surface of Venus. VEM imposes minimal requirements on the spacecraft and mission design and can therefore be added to any future Venus mission. Ideally, the VEM instrument will be combined with a high-resolution radar mapper to provide accurate topographic information, as it will be the case for the NASA Discovery VERITAS mission or the ESA EnVision M5 proposal

Regularization Strength Impact on Neural Network Ensembles

In the last decade, several approaches have been proposed for regularizing deeper and wider neural networks (NNs), which is of importance in areas like image classification. It is now common practice to incorporate several regularization approaches in the training procedure of NNs. However, the impact of regularization strength on the properties of an ensemble of NNs remains unclear. For this reason, the study empirically compared the impact of NNs built based on two different regularization strengths (weak regularization (WR) and strong regularization (SR)) on the properties of an ensemble, such as the magnitude of logits, classification accuracy, calibration error, and ability to separate true predictions (TPs) and false predictions (FPs). The comparison was based on results from different experiments conducted on three different models, datasets, and architectures. Experimental results show that the increase in regularization strength 1) reduces the magnitude of logits; 2) can increase or decrease the classification accuracy depending on the dataset and/or architecture; 3) increases the calibration error; and 4) can improve or harm the separability between TPs and FPs depending on the dataset, architecture, model type and/or FP type

The unique field-of-view and focusing budgets of PLATO

The PLAnetary Transits and Oscillations of stars mission (PLATO) is the M3 mission in ESA’s Cosmic Vision 2015-2025 Programme, see Rauer et al. (2014).1 The PLATO mission aims at detecting and characterizing extrasolar planetary systems, including terrestrial exoplanets around bright solar-type stars in the habitable zone. In order to achieve its scientific objectives, PLATO must perform uninterrupted high precision photometric monitoring of large samples of stars during long periods to detect and characterize planetary transits. The scientific payload of PLATO, developed and provided by the PLATO Mission Consortium (PMC) and ESA, is based on a multi-telescope configuration consisting of 24 “Normal” (N) cameras and 2 “Fast” (F) cameras, so as to provide simultaneously a large field of view and a large collecting aperture. The optical design is identical for all cameras and consists of a 6-lens dioptric design with a 120 mm entrance pupil and an effective field of view of more than 1000 deg2. This concept results in an overall field-of-view of more than 2000 deg², spread over 104 CCDs of 20 mega-pixels each. Associated to very accurate pointing and alignment requirements, this is a real challenge to define and breakdown precise specifications to several sub-systems in order to ensure that this overall field of view budget is achieved and verified. We propose to go through the budget that was performed for the PLATO camera and to describe how we intend to satisfy this scientific requirement. To make it more challenging, it has to be taken into account that the PLATO spacecraft will have to rotate of 90° every three months without changing its field of view (due to its orbit in L2 and the sun illumination limitations). This has to be considered in the breakdown of the budget and design of all sub-systems. A consequence of this large field of view is the difficulty to reach very good and harmonious optical performances across the field, and in a realistic depth of focus. Therefore, the focusing budget is also very challenging for the development of the PLATO cameras. We will describe the way the PLATO’s camera focusing budget has been broken down into allocations and how it is planned to be verified. To ensure optimal performances in-flight, the PLATO cameras have the extraordinary capabilities to perform re-focusing using a high precision Thermal Control System (TCS). Each individual camera on the payload can be thermally controlled independently from its neighbor to reach its own optimal operational temperature. The different consequences of this concept into the budget allocations and sub-system development will be discussed

Preparation and properties of calcium-silicate filled resins for dental restoration. Part I: chemical-physical characterization and apatite-forming ability

Based on experience gained from using the VIRTIS instrument on Venus Express to observe the surface of Venus and the new high temperature laboratory experiments, we have developed the multispectral Venus Emissivity Mapper (VEM) to study the surface of Venus. VEM imposes minimal requirements on the spacecraft and mission design and can therefore be added to any future Venus mission. Ideally, the VEM instrument will be combined with a high-resolution radar mapper to provide accurate topographic information, as it will be the case for the NASA Discovery VERITAS mission or the ESA EnVision M5 proposal

The Venus Emissivity Mapper concept

International audienceThe Venus Emissivity Mapper (VEM) is the first flight instrument specially designed with a sole focus on mapping the surface of Venus using the narrow atmospheric windows around 1μm. VEM will provide a global map of surface composition as well as redox state of the surface, providing a comprehensive picture of surface-atmosphere interaction on Venus. In addition, continuous observation of the thermal emission of the Venus will provide tight constraints on current day volcanic activity. These capabilities are complemented by measurements of atmospheric water vapor abundance as well as cloud microphysics and dynamic. Atmospheric data will allow for the accurate correction of atmospheric interference on the surface measurements and represent highly valuable science on their own. A mission combining VEM with a high-resolution radar mapper such as the NASA VOX or the ESA EnVision mission proposals in a low circular orbit will provide key insights in the divergent evolution of Venus

The Venus Emissivity Mapper (VEM) — Obtaining Global Mineralogy of Venus from Orbit

International audienceThe Venus Emissivity Mapper has a mature design with an existing laboratory prototype verifying an achievable instrument SNR of well above 1000 as well as a predicted error in the retrieval of relative emissivity of better than 1%